Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward
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Abstract
During the last two decades or so, studies on the applications of the concepts of nonlinear dynamics and chaos to hydrologic systems and processes have been on the rise. Earlier studies on this topic focused mainly on the investigation and prediction of chaos in rainfall and river flow, and further advances were made during the subsequent years through applications of the concepts to other problems (e.g. data disaggregation, missing data estimation, and reconstruction of system equations) and other processes (e.g. rainfall-runoff and sediment transport). The outcomes of these studies are certainly encouraging, especially considering the exploratory stage of the concepts in hydrologic sciences. This paper discusses some of the latest developments on the applications of these concepts to hydrologic systems and the challenges that lie ahead on the way to further progress. As for their applications, studies in the important areas of scaling, groundwater contamination, parameter estimation and optimization, and catchment classification are reviewed and the inroads made thus far are reported. In regards to the challenges that lie ahead, particular focus is given to improving our understanding of these largely less-understood concepts and also finding ways to integrate these concepts with the others. With the recognition that none of the existing one-sided ‘extreme-view’ modeling approaches is capable of solving the hydrologic problems that we are faced with, the need for finding a balanced ‘middle-ground’ approach that can integrate different methods is stressed. To this end, the viability of bringing together the stochastic concepts and the deterministic concepts as a starting point is also highlighted.
Keywords
Hydrologic systems Complexity Nonlinearity Chaos Scale Model simplification and integration Catchment classificationReferences
- Abarbanel HDI, Lall U (1996) Nonlinear dynamics of the Great Salt Lake: system identification and prediction. Clim Dyn 12:287–297CrossRefGoogle Scholar
- Amorocho J (1967) The nonlinear prediction problems in the study of the runoff cycle. Water Resour Res 3(3):861–880CrossRefGoogle Scholar
- Amorocho J, Brandstetter A (1971) Determination of nonlinear functional response functions in rainfall-runoff processes. Water Resour Res 7(5):1087–1101CrossRefGoogle Scholar
- Berndtsson R, Jinno K, Kawamura A, Olsson J, Xu S (1994) Dynamical systems theory applied to long-term temperature and precipitation time series. Trends Hydrol 1:291–297Google Scholar
- Beven KJ (2002) Uncertainty and the detection of structural change in models of environmental systems. In: Beck MB (ed) Environmental foresight and models: a manifesto. Elsevier, The Netherland, pp 227–250CrossRefGoogle Scholar
- Beven KJ (2006) On undermining the science? Hydrol Processes 20:3141–3146CrossRefGoogle Scholar
- Beven KJ, Young P (2003) Comment on ‘Bayesian recursive parameter estimation for hydrologic models’ by M. Thiemann, M. Trosset, H. Gupta, and S. Sorooshian. Water Resour Res 39(5). doi: 10.1029/2001WR001183
- Blöschl G, Sivapalan M (1995) Scale issues in hydrological modeling—a review. Hydrol Processes 9:251–290CrossRefGoogle Scholar
- Casdagli M (1992) Chaos and deterministic versus stochastic nonlinear modeling. J Royal Stat Soc B 54(2):303–328Google Scholar
- Dodov B, Foufoula-Georgiou E (2005) Incorporating the spatio-temporal distribution of rainfall and basin geomorphology into nonlinear analysis of streamflow dynamics. Adv Water Resour 28(7):711–728CrossRefGoogle Scholar
- Elshorbagy A, Simonovic SP, Panu US (2002a) Estimation of missing streamflow data using principles of chaos theory. J Hydrol 255:123–133CrossRefGoogle Scholar
- Elshorbagy A, Simonovic SP, Panu US (2002b) Noise reduction in chaotic hydrologic time series: facts and doubts. J Hydrol 256:147–165CrossRefGoogle Scholar
- Faybishenko B (2002) Chaotic dynamics in flow through unsaturated fractured media. Adv Water Resour 25(7):793–816CrossRefGoogle Scholar
- Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modeling to impacts studies: recent advances in downscaling techniques for hydrological modeling. Int J Climatol 27(12):1547–1578CrossRefGoogle Scholar
- Frazer AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33(2):1134–1140CrossRefGoogle Scholar
- Gaume E, Sivakumar B, Kolasinski M, Hazoumé L (2006) Identification of chaos in rainfall disaggregation: application to a 5-minute point series. J Hydrol 328(1–2):56–64CrossRefGoogle Scholar
- Govindaraju RS (2000) Artificial neural networks in hydrology. II: Hydrological applications. ASCE J Hydrol Eng 5:124–137CrossRefGoogle Scholar
- Grassberger P, Procaccia I (1983) Measuring the strangeness of strange attractors. Physica D 9:189–208CrossRefGoogle Scholar
- Grayson RB, Blöschl G (2000) Spatial patterns in catchment hydrology: observations and modelling. Cambridge University Press, CambridgeGoogle Scholar
- Gupta VK (2004) Emergence of statistical scaling in floods on channel networks from complex runoff dynamics. Chaos, Solitons Fractals 19:357–365CrossRefGoogle Scholar
- Gupta VK, Waymire E (1990) Multiscaling properties of spatial rainfall and river flow distributions. J Geophys Res 95(D3):1999–2009CrossRefGoogle Scholar
- Gupta VK, Duffy C, Grossman R, Krajewski W, Lall U, McCaffrey M, Milne B, Pielke R Sr, Reckow K, Swanson R (2000) A Framework for reassessment of basic research and educational priorities in hydrologic sciences. A Report to the US National Science Foundation, pp 1–40Google Scholar
- Gupta H, Thiemann M, Trosset M, Sorooshian S (2003) Reply to comment by K. Beven and P. Young on ‘Bayesian recursive parameter estimation for hydrologic models.’ Water Resour Res 39(5). doi: 10.1029/2002WR001405
- Harms AA, Campbell TH (1967) An extension to the Thomas-Fiering model for the sequential generation of streamflow. Water Resour Res 3(3):653–661CrossRefGoogle Scholar
- Henon M (1976) A two-dimensional mapping with a strange attractor. Commun Math Phys 50:69–77CrossRefGoogle Scholar
- Hill J, Hossain F, Sivakumar B (2008) Is correlation dimension a reliable proxy for the number of dominant influencing variables for modeling risk of arsenic contamination in groundwater? Stoch Environ Res Risk Assess 22(1):47–55CrossRefGoogle Scholar
- Holzfuss J, Mayer-Kress G (1986) An approach to error-estimation in the application of dimension algorithms. In: Mayer-Kress G (ed) Dimensions and entropies in chaotic systems. Springer, New York, pp 114–122Google Scholar
- Hossain F, Sivakumar B (2006) Spatial pattern of arsenic contamination in shallow wells of Bangladesh: regional geology and nonlinear dynamics. Stoch Environ Res Risk Assess 20(1–2):66–76CrossRefGoogle Scholar
- Hossain F, Anagnostou EN, Lee KH (2004) A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model. Nonlinear Processes Geophys 11:427–440Google Scholar
- Izzard CF (1966) A mathematical model for nonlinear hydrologic systems. J Geophys Res 71(20):4811–4824Google Scholar
- Jayawardena AW, Gurung AB (2000) Noise reduction and prediction of hydrometeorological time series: dynamical systems approach vs. stochastic approach. J Hydrol 228:242–264CrossRefGoogle Scholar
- Jayawardena AW, Lai F (1994) Analysis and prediction of chaos in rainfall and stream flow time series. J Hydrol 153:23–52CrossRefGoogle Scholar
- Jayawardena AW, Li WK, Xu P (2002) Neighborhood selection for local modeling and prediction of hydrological time series. J Hydrol 258:40–57CrossRefGoogle Scholar
- Jin YH, Kawamura A, Jinno K, Berndtsson R (2005) Nonlinear multivariate analysis of SOI and local precipitation and temperature. Nonlinear Processes Geophys 12:67–74Google Scholar
- Kavvas ML (2003) Nonlinear hydrologic processes: conservation equations for determining their means and probability distributions. ASCE J Hydrol Eng 8(2):44–53CrossRefGoogle Scholar
- Khan S, Ganguly AR, Saigal S (2005) Detection and predictive modeling of chaos in finite hydrological time series. Nonlinear Processes Geophys 12:41–53Google Scholar
- Kim HS, Eykholt R, Salas JD (1999) Nonlinear dynamics, delay times, and embedding windows. Physica D l27(1–2):48–60CrossRefGoogle Scholar
- Kirchner JW (2006) Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resour Res 42:W03S04. doi: 10.1029/2005WR004362 CrossRefGoogle Scholar
- Klemes V (1978) Physically based stochastic hydrologic analysis. Adv Hydrosci 11:285–352Google Scholar
- Koutsoyiannis D (2006) On the quest for chaotic attractors in hydrological processes. Hydrol Sci J 51(6):1065–1091CrossRefGoogle Scholar
- Koutsoyiannis D, Pachakis D (1996) Deterministic chaos versus stochasticity in analysis and modeling of point rainfall series. J Geophys Res 101(D21):26441–26451CrossRefGoogle Scholar
- Laio F, Porporato A, Revelli R, Ridolfi L (2003) A comparison of nonlinear flood forecasting methods. Water Resour Res 39(5): 10.1029/2002WR001551
- Laio F, Porporato A, Ridolfi L, Tamea S (2004) Detecting nonlinearity in time series driven by non-Gaussian noise: the case of river flows. Nonlinear Processes Geophys 11:463–470Google Scholar
- Lambrakis N, Andreou AS, Polydoropoulos P, Georgopoulos E, Bountis T (2000) Nonlinear analysis and forecasting of a brackish karstic spring. Water Resour Res 36(4):875–884CrossRefGoogle Scholar
- Leibert W, Schuster HG (1989) Proper choice of the time delay for the analysis of chaotic time series. Phys Lett 141:386–390CrossRefGoogle Scholar
- Lin GF (1990) Parameter estimation for seasonal to subseasonal disaggregation. J Hydrol 120:65–77CrossRefGoogle Scholar
- Lisi F, Villi V (2001) Chaotic forecasting of discharge time series: a case study. J Am Water Resour Assoc 37(2):271–279CrossRefGoogle Scholar
- Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141CrossRefGoogle Scholar
- Manzoni S, Porporato A, D’Odorico P, Laio F, Rodriguez-Iturbe I (2004) Soil nutrient cycles as a nonlinear dynamical system. Nonlinear Processes Geophys 11:589–598Google Scholar
- McDonnell JJ, Woods RA (2004) On the need for catchment classification. J Hydrol 299:2–3Google Scholar
- Nordstrom KM, Gupta VK, Chase TN (2005) Role of the hydrological cycle in regulating the planetary climate system of a simple nonlinear dynamical model. Nonlinear Processes Geophys 12:741–753Google Scholar
- Packard NH, Crutchfield JP, Farmer JD, Shaw RS (1980) Geometry from a time series. Phys Rev Lett 45(9):712–716CrossRefGoogle Scholar
- Phillips JD (2006) Deterministic chaos and historical geomorphology: a review and look forward. Geomorphology 76:109–121CrossRefGoogle Scholar
- Phillips JD, Walls MD (2004) Flow partitioning and unstable divergence in fluviokarst evolution in central Kentucky. Nonlinear Processes Geophys 11:371–381Google Scholar
- Phoon KK, Islam MN, Liaw CY, Liong SY (2002) A practical inverse approach for forecasting of nonlinear time series analysis. ASCE J Hydrol Eng 7(2):116–128CrossRefGoogle Scholar
- Porporato A, Ridolfi R (1997) Nonlinear analysis of river flow time sequences. Water Resour Res 33(6):1353–1367CrossRefGoogle Scholar
- Porporato A, Ridolfi R (2001) Multivariate nonlinear prediction of river flows. J Hydrol 248(1–4):109–122CrossRefGoogle Scholar
- Puente CE, Obregon N (1996) A deterministic geometric representation of temporal rainfall. Results for a storm in Boston. Water Resour Res 32(9):2825–2839CrossRefGoogle Scholar
- Regonda S, Sivakumar B, Jain A (2004) Temporal scaling in river flow: can it be chaotic? Hydrol Sci J 49(3):373–385CrossRefGoogle Scholar
- Regonda S, Rajagopalan B, Lall U, Clark M, Moon YI (2005) Local polynomial method for ensemble forecast of time series. Nonlinear Processes Geophys 12:397–406Google Scholar
- Rodriguez-Iturbe I, Rinaldo A (1997) Fractal River Basins: Chance and Self-Organization. Cambridge University Press, Cambridge, UKGoogle Scholar
- Rodriguez-Iturbe I, De Power FB, Sharifi MB, Georgakakos KP (1989) Chaos in rainfall. Water Resour Res 25(7):1667–1675CrossRefGoogle Scholar
- Salas JD, Smith RA (1981) Physical basis of stochastic models of annual flows. Water Resour Res 17(2):428–430CrossRefGoogle Scholar
- Salas JD, Kim HS, Eykholt R, Burlando P, Green TR (2005) Aggregation and sampling in deterministic chaos: implications for chaos identification in hydrological processes. Nonlinear Processes Geophys 12:557–567Google Scholar
- Sangoyomi TB, Lall U, Abarbanel HDI (1996) Nonlinear dynamics of the Great Salt Lake: Dimension estimation. Water Resour Res 32(1):149–159CrossRefGoogle Scholar
- Schertzer D, Tchiguirinskaia I, Lovejoy S, Hubert P, Bendjoudi H (2002) Which chaos in the rainfall-runoff process? A discussion on ‘Evidence of chaos in the rainfall-runoff process’ by Sivakumar et al. Hydrol Sci J 47(1):139–147Google Scholar
- She N, Basketfield D (2005) Streamflow dynamics at the Puget Sound, Washington: application of a surrogate data method. Nonlinear Processes Geophys 12:461–469Google Scholar
- Sivakumar B (2000) Chaos theory in hydrology: important issues and interpretations. J Hydrol 227(1–4):1–20CrossRefGoogle Scholar
- Sivakumar B (2001a) Is a chaotic multi-fractal approach for rainfall possible? Hydrol Processes 15(6):943–955CrossRefGoogle Scholar
- Sivakumar B (2001b) Rainfall dynamics at different temporal scales: A chaotic perspective. Hydrol Earth Syst Sci 5(4):645–651Google Scholar
- Sivakumar B (2002) A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. J Hydrol 258:149–162CrossRefGoogle Scholar
- Sivakumar B (2004a) Chaos theory in geophysics: past, present and future. Chaos, Solitons Fractals 19(2):441–462CrossRefGoogle Scholar
- Sivakumar B (2004b) Dominant processes concept in hydrology: moving forward. Hydrol Processes 18(12):2349–2353CrossRefGoogle Scholar
- Sivakumar B (2005a) Hydrologic modeling and forecasting: role of thresholds. Environ Model Softw 20(5):515–519CrossRefGoogle Scholar
- Sivakumar B (2005b) Correlation dimension estimation of hydrologic series and data size requirement: myth and reality. Hydrol Sci J 50(4):591–604CrossRefGoogle Scholar
- Sivakumar B (2007) Nonlinear determinism in river flow: prediction as a possible indicator. Earth Surf Process Landf 32(7):969–979CrossRefGoogle Scholar
- Sivakumar B (2008a) Dominant processes concept, model simplification and classification framework in catchment hydrology. Stoch Env Res Risk Assess 22(6):737–748. doi: 10.1007/s00477-007-0183-5 CrossRefGoogle Scholar
- Sivakumar B (2008b) Undermining the science or undermining Nature? Hydrol Processes 22(6):893–897CrossRefGoogle Scholar
- Sivakumar B (2008c) Climate and its impacts on water resources: a case for nonlinear data downscaling approaches. Geophys Res Abs 10, EGU2008-A-00218Google Scholar
- Sivakumar B, Chen J (2007) Suspended sediment load transport in the Mississippi River basin at St. Louis: temporal scaling and nonlinear determinism. Earth Surf Process Landf 32(2):269–280CrossRefGoogle Scholar
- Sivakumar B, Jayawardena AW (2002) An investigation of the presence of low-dimensional chaotic behavior in the sediment transport phenomenon. Hydrol Sci J 47(3):405–416Google Scholar
- Sivakumar B, Liong SY, Liaw CY, Phoon KK (1999a) Singapore rainfall behavior: chaotic? ASCE J Hydrol Eng 4(1):38–48CrossRefGoogle Scholar
- Sivakumar B, Phoon KK, Liong SY, Liaw CY (1999b) A systematic approach to noise reduction in chaotic hydrological time series. J Hydrol 219(3/4):103–135CrossRefGoogle Scholar
- Sivakumar B, Berndttson R, Olsson J, Jinno K (2001a) Evidence of chaos in the rainfall-runoff process. Hydrol Sci J 46(1):131–145CrossRefGoogle Scholar
- Sivakumar B, Sorooshian S, Gupta HV, Gao X (2001b) A chaotic approach to rainfall disaggregation. Water Resour Res 37(1):61–72CrossRefGoogle Scholar
- Sivakumar B, Berndtsson R, Olsson J, Jinno K (2002a) Reply to ‘which chaos in the rainfall-runoff process?’ by Schertzer et al. Hydrol Sci J 47(1):149–158Google Scholar
- Sivakumar B, Jayawardena AW, Fernando TMGH (2002b) River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. J Hydrol 265(1–4):225–245CrossRefGoogle Scholar
- Sivakumar B, Persson M, Berndtsson R, Uvo CB (2002c) Is correlation dimension a reliable indicator of low-dimensional chaos in short hydrological time series? Water Resour Res 38(2). doi: 10.1029/2001WR000333
- Sivakumar B, Wallender WW, Puente CE, Islam MN (2004) Streamflow disaggregation: a nonlinear deterministic approach. Nonlinear Processes Geophys 11:383–392Google Scholar
- Sivakumar B, Harter T, Zhang H (2005) Solute transport in a heterogeneous aquifer: a search for nonlinear deterministic dynamics. Nonlinear Processes Geophys 12:211–218Google Scholar
- Sivakumar B, Wallender WW, Horwath WR, Mitchell JP, Prentice SE, Joyce BA (2006) Nonlinear analysis of rainfall dynamics in California’s Sacramento Valley. Hydrol Processes 20(8):1723–1736CrossRefGoogle Scholar
- Sivakumar B, Jayawardena AW, Li WK (2007) Hydrologic complexity and classification: a simple data reconstruction approach. Hydrol Processes 21(20):2713–2728CrossRefGoogle Scholar
- Sivapalan M, Blöschl G, Zhang L, Vertessy R (2003) Downward approach to hydrological prediction. Hydrol Processes 17:2101–2111CrossRefGoogle Scholar
- Takens F (1981) Detecting strange attractors in turbulence. In: Rand DA, Young LS (eds) Dynamical systems and turbulence, lecture notes in mathematics 898, Springer, Berlin, pp 366–381Google Scholar
- Tarboton DG, Sharma A, Lall U (1998) Disaggregation procedures for stochastic hydrology based on nonparametric density estimation. Water Resour Res 34(1):107–119CrossRefGoogle Scholar
- Tsonis AA, Georgakakos KP (2005) Observing extreme events in incomplete state spaces with application to rainfall estimation from satellite images. Nonlinear Processes Geophys 12:195–200Google Scholar
- Tsonis AA, Triantafyllou GN, Elsner JB, Holdzkom JJ II, Kirwan AD Jr (1994) An investigation on the ability of nonlinear methods to infer dynamics from observables. Bull Amer Meteor Soc 75:1623–1633CrossRefGoogle Scholar
- Wagener T, Sivapalan M, Troch PA, Woods RA (2007) Catchment classification and hydrologic similarity. Geogr Compass 1(4):901–931. doi: 10.1111/j.1749-8198.2007/00039.x CrossRefGoogle Scholar
- Wang Q, Gan TY (1998) Biases of correlation dimension estimates of streamflow data in the Canadian prairies. Water Resour Res 34(9):2329–2339CrossRefGoogle Scholar
- Wilcox BP, Seyfried MS, Matison TM (1991) Searching for chaotic dynamics in snowmelt runoff. Water Resour Res 27(6):1005–1010CrossRefGoogle Scholar
- Woods RA (2002) Seeing catchments with new eyes. Hydrol Processes 16:1111–1113CrossRefGoogle Scholar
- Young PC, Beven KJ (1994) Data-based mechanistic modeling and rainfall-flow non-linearity. Environmetrics 5(3):335–363CrossRefGoogle Scholar
- Zhou Y, Ma Z, Wang L (2002) Chaotic dynamics of the flood series in the Huaihe River Basin for the last 500 years. J Hydrol 258:100–110CrossRefGoogle Scholar